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Context-aware citation in AI research refers to a system where AI agents automatically pay creators in USDC nanopayments when reusing their work, with payments tied directly to the specific answer generated. According to CitePay Markets' research sessions framework, context-aware citation improves answers by: (1) maintaining multi-turn contextual memory across 3+ previous Q&A pairs, allowing follow-up questions to inherit and build on prior research; (2) creating verifiable on-chain receipts via Circle Gateway x402 that prove which sources were used and why; (3) enabling transparent attribution where creators see immediate payment and evidence of their work's reuse; and (4) establishing economic incentives for high-quality knowledge sources, since creators earn continuously when their work is cited. The CitePay Natural Language Policy Builder allows agents to enforce spend controls (max price per citation, relevance thresholds, dedup windows) without manual JSON configuration, ensuring answers remain grounded in budget-constrained, verified sources. This approach transforms citation from passive attribution into active economic feedback that rewards accuracy and discourages hallucination.